Modelling genetic and genomic interactions underlying gene expression and complex traits

This study focuses on integrating and applying computational techniques for modelling quantitative traits and complex diseases, such as hypertension and diabetes, using the rat model system and translating the findings to humans. Complex disease traits are heritable, highly polygenic, and influenced by environmental factors. Human studies, like Genome Wide Association Studies (GWAS), have identified many genetic determinants underlying these traits but have provided little information about the functional effects of these variants and mechanisms regulating the disease. This study takes a systems-level approach for looking at the genetic regulation of complex traits in the rat by analysing multiple phenotypes, genomewide genetic variation and gene expression data in multiple tissues. I integrated these multi-modality datasets in the BXH/HXB rat Recombinant Inbred (RI) lines, an established model of the human metabolic syndrome, to identify candidate genes, pathways and networks associated with complex disease phenotypes. I evaluated methods for Expression Quantitative Trait Locus (eQTL) analysis and used sparse Bayesian regression approaches to map eQTLs in the RI lines, delineating a new, large eQTL data resource for the rat genetic community. I have also developed and applied signal processing and time series analysis methods to physiological traits to extract more detailed indices of blood pressure, and integrated these with genetic, expression and eQTL data to inform on the regulation of these traits. Then, using publicly available data, I used comparative genomics approaches to elucidate a set of genes and pathways that can play a role in human diseases. This study has provided a valuable resource for future work in the rat, by means of new eQTLs in multiple tissues, and physiological time series phenotypes and approaches. This has enabled an integrative analysis of these data to give new insights into the regulation of complex traits in rats and humans

Angiogenic MicroRNAs Linked to Incidence and Progression of Diabetic Retinopathy in Type 1 Diabetes

Circulating microRNAs (miRNAs) have emerged as novel biomarkers of diabetes. The current study focuses on the role of circulating miRNAs in patients with type 1 diabetes and their association with diabetic retinopathy. A total of 29 miRNAs were quantified in serum samples (n = 300) using a nested case-control study design in two prospective cohorts of the DIabetic REtinopathy Candesartan Trial (DIRECT): PROTECT-1 and PREVENT-1. The PREVENT-1 trial included patients without retinopathy at baseline; the PROTECT-1 trial included patients with nonproliferative retinopathy at baseline. Two miRNAs previously implicated in angiogenesis, miR-27b and miR-320a, were associated with incidence and with progression of retinopathy: the odds ratio per SD higher miR-27b was 0.57 (95% CI 0.40, 0.82; P = 0.002) in PREVENT-1, 0.78 (0.57, 1.07; P = 0.124) in PROTECT-1, and 0.67 (0.50, 0.92; P = 0.012) combined. The respective odds ratios for higher miR-320a were 1.57 (1.07, 2.31; P = 0.020), 1.43 (1.05, 1.94; P = 0.021), and 1.48 (1.17, 1.88; P = 0.001). Proteomics analyses in endothelial cells returned the antiangiogenic protein thrombospondin-1 as a common target of both miRNAs. Our study identifies two angiogenic miRNAs, miR-320a and miR-27b, as potential biomarkers for diabetic retinopathy

Molecular subgroups of intrahepatic cholangiocarcinoma discovered by single-cell RNA sequencing–assisted multiomics analysis

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumor type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. Here, we constructed two independent cohorts from two clinic centers. A comprehensive multiomics analysis of ICC via proteomic, whole-exome sequencing (WES), and single-cell RNA sequencing (scRNA-seq) was performed. Novel ICC tumor subtypes were derived in the training cohort (n = 110) using proteomic signatures and their associated activated pathways, which were further validated in a validation cohort (n = 41). Three molecular subtypes, chromatin remodeling, metabolism, and chronic inflammation, with distinct prognoses in ICC were identified. The chronic inflammation subtype was associated with a poor prognosis. Our random forest algorithm revealed that mutation of lysine methyltransferase 2D (KMT2D) frequently occurred in the metabolism subtype and was associated with lower inflammatory activity. scRNA-seq further identified an APOE+C1QB+ macrophage subtype, which showed the capacity to reshape the chronic inflammation subtype and contribute to a poor prognosis in ICC. Altogether, with single-cell transcriptome-assisted multiomics analysis, we identified novel molecular subtypes of ICC and validated APOE+C1QB+ tumor-associated macrophages as potential immunotherapy targets against ICC